Environmental drivers of movement in a threatened seabird: insights from a mechanistic model and implications for conservation

Determining the drivers of movement of different life‐history stages is crucial for understanding age‐related changes in survival rates and, for marine top predators, the link between fisheries overlap and incidental mortality (bycatch), which is driving population declines in many taxa. Here, we combine individual tracking data and a movement model to investigate the environmental drivers and conservation implications of divergent movement patterns in juveniles (fledglings) and adults of a threatened seabird, the white‐chinned petrel (Procellaria aequinoctialis).


| INTRODUC TI ON
Determining the processes that influence the capacity and motivation for movement within and among species constitutes a primary goal for ecologists, given the far-reaching consequences for individual fitness, population dynamics and conservation (Arjo, Huenefeld, & Nolte, 2007;Munday, 2001;Ribera, Foster, & Vogler, 2003). In most animals, the mechanisms shaping the initial movements of juveniles away from their natal grounds and subsequent habitat use are poorly known, yet this period represents a critical life-history stage when mortality is high (Gaillard, Festa-Bianchet, | 1317 FRANKISH et Al. movements of juvenile white-chinned petrels and their overlap with fisheries have not been quantified. Here, we analysed movement data from juvenile and adult white-chinned petrels tracked from South Georgia, south-west Atlantic Ocean, which is the largest global population and is declining (Berrow et al. 2000a). Our principal aims were to (a) investigate initial dispersal patterns of juveniles during the post-fledging period; (b) apply a mechanistic movement model to identify the potential drivers of movement patterns of different life-history stages; and in the path of prevailing westerly winds, and thus, the main wintering site for this population, the Patagonian Shelf, is directly accessed by flying into headwinds, which is energetically costly (Phillips, Silk, Croxall, & Afanasyev, 2006;Weimerskirch et al., 2000). This study system therefore offers an ideal opportunity to investigate the relative influence of different environmental factors on long-distance movement in birds: attraction to foraging resources and the effect of wind on energetic costs of movement (Somveille et al., 2015;Vansteelant, Shamoun-Baranes, van Manen, van Diermen, & Bouten, 2017). We hypothesize that wind speed and direction is more likely to determine the trajectories of naïve individuals with no prior flight or foraging experience, whereas experienced adults should migrate directly towards known foraging areas. December 2014 and 13 January 2015 to track movements during the subsequent non-breeding period. Geolocators were attached by cable tie to a plastic leg ring, and all devices were retrieved in the following austral breeding season (14 December 2015 to 7 January 2016). The loggers measured light in the range of 1.1 to 74418 lux (maximum recorded at 5-min intervals), temperature every 20 min of continuous wet (maximum, minimum and mean saved every 4 hr) and tested for saltwater immersion every 6 s. The immersion data were used for generating the speed parameters used in the processing of tracks from non-breeding adults (see below, Table S1.1). In all cases, the total mass of devices including attachments was less than the 3% threshold of body mass beyond which deleterious effects are more common in pelagic seabirds (Phillips, Xavier, & Croxall, 2003).

| Deployments and tracking data processing
Platform Terminal Transmitters and GPS tracks were processed using an iterative forward/backward-averaging filter (McConnell, Chambers, & Fedak, 1992) to remove any locations, which required sustained flight speeds above 80 km/hr (Berrow, Wood, & Prince, 2000). Data from GPS loggers and PTTs (during the ON periods only) were interpolated at hourly intervals to obtain regular positions, as this time step represented the coarsest tracking interval across datasets. GPS tracks from breeding adults were resampled to the same duty cycle as the tracks from juveniles in order to compare movement parameters between these life-history stages using an equivalent sampling regime.
Locations were estimated for adults tracked during the non-breeding period using the raw light intensities from the geolocators processed according to Merkel et al. (2016, see Appendix S1 for details). GLS data were not interpolated, as the estimated locations correspond to local midday and midnight. Juvenile tracks were resampled to 12-hr intervals to allow for comparisons of their movement parameters with those of the non-breeding adults. GLS locations were cropped to the juvenile departure dates from the colony to allow for the comparison of utilization distribution and overlap with fishing effort.

| Comparing movements and distributions between life-history stages
We compared the spatial distributions and movement characteristics (maximum range and average longitude; metrics #1 and #2 below) of juveniles and non-breeding adults at large spatial scales based on the twice-daily fixes from the PTTs and geolocators, and the movement characteristics (speed and track sinuosity; metrics #3 and #4 below) at small spatial scales based on the hourly interpolated PTT fixes and the GPS data from incubating adults, respectively (see above).
The movement metrics were those commonly used for analyses of animal trajectories (Calenge, Dray, & Royer-Carenzi, 2009): (a) maximum range (maximum distance from the colony in km, calculated using function "spDistsN1" in package "sp"); (b) longitude averaged over weekly time periods for juveniles, and for the first eight weeks, post-departure, of non-breeding adults (corresponding to the maximum duration of a juvenile track; 57 days); (c) speed (in km/hr); and (d) track sinuosity (calculated as follows: S = 1-D a /D b , with D a the beeline distance between the first and last location of every "ON" portion of the trip and D b the real distance travelled between the two locations). Speed and track sinuosity were also averaged over a weekly time period for juveniles to examine changes over time, as with metrics #1 and #2. Speed was square-root-transformed to improve data spread.
Linear mixed-effects models were run with each movement metric as the response variable and individual ID as a random effect, testing for differences between life-history stages as a function of time. For models with maximum range and longitude, the covariates included life-history stage (a factor with two levels; non-breeding adult NB, and juvenile JUV), weeks since departure from the colony (WEEK; factor with eight levels; 1-8) and their interaction. For models with speed and sinuosity, covariates included life-history stage (a factor with two levels; incubating INC adults, and juvenile JUV). Weekly differences were further investigated in juveniles only, where WEEK was again included as a factor with eight levels (1-8), to test whether juveniles showed signs of learning in terms of their flight abilities. For each model set, all possible combinations of predictors were computed and models were ranked according to Akaike information criterion (AICc) values, where the most supported model(s) were considered to be those within 2Δ AICc of the top model (Burnham & Anderson, 2004). Candidate models were excluded from this set if there were simpler nested versions with lower ΔAICc values (Arnold, 2010).
To determine whether juvenile and non-breeding adult whitechinned petrels differed in their weekly spatial distributions, we calculated utilization distribution (UD) kernels using the R package "adehabitatHR" (Calenge, 2006). We first carried out a resampling procedure to determine whether sample sizes were large enough to represent population-level space use (Tables S2.1 and   Tables S2.2 To control for differences in individual track duration, separate UDs were generated weekly for each bird and then weighted by the proportion of locations from each bird with respect to the total number for all birds for a given stage-week combination. Weighted individual UDs were then summed to create weekly UDs for each life-history stage. A grid size of 5 km and a smoothing parameter of 185 km were chosen to account for geolocator error and applied to all datasets in this comparison to control for differences in location error from each type of device (Merkel et al., 2016). We then compared observed vs. randomized overlap in core and general use area between stages for each week using Bhattacharyya's affinity (BA) and previously established methods (Breed, Bowen, McMillan, & Leonard, 2006; see Appendix S2c for details).

| Mechanistic movement model
A two-parameter mechanistic model was used to investigate the potential drivers of juvenile and non-breeding adult movements (Revell & Somveille, 2017). This model simulates the movements of a bird away from a given location and through a potential landscape defined by two environmental factors: (a) attraction to chlorophyll a concentration (a proxy for food resources) and (b) the effect of wind (i.e. assistance). Both variables were modelled as described in Revell and Somveille (2017) Zhang, Bates, & Reynolds, 2006). These two datasets were averaged over the period from 2003 to 2015 to represent longterm conditions (i.e. a climatology) in the study area. We chose to use climatologies both to minimize gaps in measurements due to cloud cover, and because we hypothesize that differences in movement strategies of adults and juveniles are linked to longer term (i.e. evolutionary) environmental processes (Suryan, Santora, & Sydeman, 2012;Weimerskirch et al., 2000;Woodward & Gregg, 1998) Wind speed and direction were compared between the NOAA and ASCAT datasets in years when both were available (2008-2011); differences were found to be minimal and did not influence model simulation outcomes (Appendix S3). All environmental datasets were accessed in December 2019.
Within this potential landscape, the model framework assumes that birds are inherently attracted to resources, and we ran a range of scenarios varying the importance of the wind component relative to this attraction, characterized by the parameter a. Low values of a correspond to scenarios in which the effect of wind on movement patterns is minimal, and thus attraction to resources dominates, whereas progressively higher values of a reflect an increased role of wind on bird trajectories (Revell & Somveille, 2017). An initial search of the parameter space of a revealed that there was no further variation in results below a = 0.005 and above a = 0.2, and we interpreted these extreme values as scenarios in which effects of resource attraction and wind-assisted movement dominated, respectively. Simulations were then run for values of a as multiples of 0.015 from 0.005 to 0.2, to investigate a broad range of scenarios (84 simulations in total). Another unknown parameter kT, representing the degree of randomness in the movement decisions, was set to a low value (0.05; Revell & Somveille, 2017). All simulations began at Bird Island and were set to run for 3 months starting from April, the only month in which both non-breeding adults (6/16 birds) and juveniles (6/13 birds) departed from the colony in our study. Simulations were run 6 times for each value of a to capture the behaviour of both life-history stages.
The similarity between the resulting simulated and observed (the 6 juvenile and 6 non-breeding adults which departed the colony in April) tracks was investigated using dynamic time warping (DTW), as this measure allows for the comparison of trajectories that may vary in time or speed (Cleasby et al., 2019;Ranacher & Tzavella, 2014).
Pairwise DTW measures were computed for all tracks (simulated and observed), and the resulting distance matrix was examined using hierarchical clustering with a "ward-D2" linkage, which minimizes within-cluster variance. Tracks were clustered to investigate which scenario of simulated tracks most closely aligned with observed adult and juvenile tracks using an increasing number of groups (k) ranging in value between 2 and 5, at which points the tracks pertaining to a particular group (simulated, juvenile or non-breeding adult) were clustered separately.

| Juvenile and non-breeding adult distributions and overlap with fisheries
We analysed overlap by week of the distribution of juveniles and non-breeding adults with longline fishing effort based on vessel movements to investigate potential difference in susceptibility to bycatch. Weekly core UDs were generated for each bird, resampled to a 0.1 × 0.1° resolution, and overlaid on a 0.1 × 0.1° grid of weekly fishing effort. Summed fishing effort per week for pelagic and demersal longline fisheries was collated from the Global Fishing Watch dataset (Global Fishing Watch [GFW], 2019, Option = "drifting longline"). GFW provides information on daily fishing effort (hours) of vessels transmitting their location using an automatic identification system (AIS). As AIS is fitted to only 50-75% of active vessels that are over 24 m in length McCauley et al., 2016;Sala et al., 2018;Shepperson et al., 2018), we determined whether the GFW dataset accurately captured longline fishing effort of all important fleets within the study area (south Atlantic Ocean) and period (April-July 2015) by contrasting the overlap of bird distributions with pelagic longline fishing effort using both AIS data (from GFW) and log-book effort data reported to the International Commission for the Conservation of Atlantic Tunas (ICCAT Task II Effort; https://www.iccat.int/en/acces ingdb.html [accessed April 2020]). As effort data from ICCAT were available at monthly, 5 × 5° resolution, monthly core UDs were generated for each bird for April and May (when sample sizes were high for juveniles), and resampled to a 5 × 5° resolution. Fishing intensity grids were obtained at the same spatial-temporal resolution for GFW data by summing 0.1 × 0.1° fishing effort (hours fishing) that fell within each 5 × 5° Linear mixed-effect models were run to test for differences over time in overlap of juveniles and non-breeding adults with GFW fishing activity. The overlap score (hours/week) was modelled as the response variable with individual ID as a random effect, and life-history stage (factor with two levels: non-breeding adult NB and juvenile JUV), and weeks since departure from the colony (Week; factor with eight levels; 1-8) were included as covariates. The overlap score was square-root-transformed to improve data spread. Model selection was conducted using the approach detailed in Section 2.2.
Unless otherwise indicated, all means in the Results are given ± standard error (SE).

| Distribution and movement characteristics of juveniles and adults
The juvenile white-chinned petrels fledged in April-May 2015 and dispersed in a northerly direction from South Georgia over a wide area of the south Atlantic Ocean (53.7°W-4.7°E). Individuals were tracked for periods of 1-57 days, with the last transmissions received by the ARGOS system in July 2015 (Figure 1). The nonbreeding adults tracked using geolocators began migration between late January and early May 2015 and spent the non-breeding period F I G U R E 1 Distribution of adult (incubating, INC; and non-breeding, NB) and juvenile (JUV) white-chinned petrels Procellaria aequinoctialis tracked from Bird Island (South Georgia) during the 2014/15 breeding season and subsequent winter. Incubating (n = 12) and non-breeding (n = 16) adults were tracked using global positioning system (GPS) loggers and global location sensors (GLS), respectively, and juveniles (n = 13) using Platform Terminal Transmitters (PTT) Movement parameters of juvenile and non-breeding adults differed in the weeks following departure from the colony (Table 1a and Table S5. American continent (to 47.4 ± 3.1°W in week 7, Figure 2b). Both the core and general use areas of the tracked juveniles differed significantly from those of non-breeding adults ( Figure 3 and Table 2), although there was some overlap from the fourth week onwards, as juveniles moved towards waters off south-east Brazil and Uruguay (Table 2).
There was little evidence of an effect of life-history stage or number of weeks post-departure on the flight metrics of incubating adults and juveniles (Table 1a and Table S5.1 for full model selection and Figure 4a-d). While the two models for flight speed-with and without the covariate life-history stage-were both supported (< 2 ΔAICc), the former predicted that juveniles flew only slightly faster (by approximately 3.7 km/hr) than incubating adults (Table 1 and Figure 4b). Track sinuosity was also similar between life-history stages (0.22 ± 0.01, Table 1a and Figure 4a), and there was no effect of the number of weeks since fledging on the average speed and sinuosity of juveniles (Table 1a and Figures 4c,d).

| Mechanistic movement model
Hierarchical clustering of pairwise DTW distances provided strong evidence that, when compared to the simulated tracks, the ob-  All birds were tracked from Bird Island (South Georgia) during the 2014/15 breeding season and subsequent winter. Models including all possible combinations of the predictor variables were considered and ranked according to Akaike information criterion (AICc). Those reported above were within 2Δ of the best model. "Life-history stages considered" indicates the life-history stages compared for a given movement metric; "x" predictor variables retained in the best models; "NA" variables that were not modelled; "df" the degrees of freedom; "Week" the weeks following departure from the colony; and "AICcw" the AICc weight, the relative probability that a given model is the best model. See Table S5.1 for all combinations of predictors considered for model selection.  Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week Simulated tracks were in a north-easterly direction until 30-45°S, at which point they turned directly east towards the productivity hotspot located off the coast of Namibia (Figure 5a). It is worth noting that one juvenile which departed from the colony in May also TA B L E 2 Observed and randomized overlap (Bhattacharyya's affinity index) of utilization distributions (UD) between juvenile (JUV) and non-breeding adult (NB) white-chinned petrels Procellaria aequinoctialis tracked over the first eight weeks since their departure from Bird Island (South Georgia) during the 2014/15 breeding season and subsequent winter Randomized overlaps are shown as mean ± SD, and p represents the proportion of randomized overlaps that were smaller than the observed. headed in this direction before the transmitter ran out, suggesting that heading towards the African coast may be a rare strategy conducted by a minority of individuals. Two simulated tracks went west instead, but towards more northerly locations along the South American coast, which would explain why they did not group into Cluster 2 for k = 3.

| Spatial overlap with longline fishing vessel activity
As a result of differences in their at-sea distributions, non-breeding adults and juveniles varied in the location and extent of their overlap with demersal and pelagic longline fishing activity (Figures 3, 6 & 7 and Table 1b and Table S5.1 for full model selection). On average, there was less longline fishing activity (by c. 130 hr, from vessels with active AIS) in the 0.1 × 0.1° grid cells used by juveniles than those used by non-breeding adults (Figure 6a), mainly because juveniles spent the first few weeks post-fledging in areas of the south Atlantic Ocean where few vessels operate (Figure 3). Although overlap scores were lower for juveniles, they nevertheless overlapped with fishing vessels with active AIS from the first week after fledging from South Georgia. In addition, average scores increased over the study period, from a low of 0.03 hr in week 2 to a high of 9.55 hr in week 8, as individuals reached the coastal waters of Uruguay and south-east Brazil (Figures 3 and 6). In this region, however, there are likely to be a large proportion of vessels operating without active AIS, as coarserscale analyses using ICCAT effort data revealed substantial overlap of juveniles with the fleets of Taiwan and Brazil, while overlap was negligible using GFW effort data (Figure 7).
The main areas of fisheries overlap were around South Georgia, along the coast from Argentina to south-east Brazil, around Tristan da Cunha, and off Namibia (Figures 3 and 7).  overlapped more with longline fishing vessels with active AIS because they migrated to the productive Patagonian Shelf, where fishing activity was much more concentrated (Figure 3). Overlap was high from Tierra Del Fuego to south-east Brazil, and dominated by the fleets of Argentina (weeks 1-8), followed by Cambodia, China, South Korea, and, to a lesser extent, Uruguay, Chile, Ukraine, Spain, Taiwan, Portugal and the Falkland Islands (Figure 6b).

| D ISCUSS I ON
Through combining individual tracking data and a mechanistic model, we found that juveniles and adults differed in their movement patterns and that movements were best explained by different processes: wind-assisted movement in juveniles and attraction to productive regions, irrespective of wind conditions, in adults. While

| Ontogeny of movement strategies: learned vs. innate behaviour
The capacity for long-distance movement is widespread in the animal kingdom, and movement strategies are commonly thought to develop through a combination of learning (social or individual) or genetic programming in young life-history stages (Putman et al., 2014;Weinrich, 2008). In many species of birds (terrestrial and marine), young individuals may follow one or both of their parents on their first foraging flight or migration, allowing them to learn a migration route and the location of feeding areas, or to develop their foraging skills (Guo, Cao, Peng, Zhao, & Tang, 2010;Harding, Van Pelt, Lifjeld, & Mehlum, 2004;Regehr, Smith, Arquilla, & Cooke, 2001). In contrast, juvenile white-chinned petrels fledge independently from their parents and, as our study showed, rapidly flew large distances from the colony. Remarkably, their flight speeds and sinuosity were similar to those of breeding adults, suggesting comparable flight capability. Young individuals of other petrel and albatross species also disperse rapidly away from their natal colony, suggesting an innate ability to orientate with respect to wind direction, and fly with a high level of efficiency immediately after fledging (Alderman et al., 2010;de Grissac et al., 2016;Riotte-Lambert & Weimerskirch, 2013). This is not typical of other seabird taxa, however, which instead show progressive improvements in their flight performance with the number of days since fledging (Corbeau, Prudor, Kato, & Weimerskirch, 2019;Mendez, Prudor, & Weimerskirch, 2019;Yoda et al., 2004).
Navigating across the seemingly featureless pelagic ocean seems challenging, but innate flight skills may allow juveniles to search for patchily distributed resources across large spatial scales, similarly to adults (Adams, Brown, & Nagy, 1986;Alerstam, Hedenström, & Åkesson, 2003;Warham, 1990;Weimerskirch et al., 2000). Indeed, when the juvenile tracks were compared to model simulations, the best match was with environmental scenarios dominated by wind, suggesting movements of juveniles are strongly influenced by prevailing wind patterns in the south Atlantic. As the model assumes some inherent attraction to resources (Revell & Somveille 2017), even for wind-dominated scenarios, we were unable to simulate a scenario whereby there was full passive drift (like sea turtles with ocean currents; e.g. Scott, Marsh, & Hays, 2014). However, as prevailing winds at 40-60°S are westerly, we presume that under a full-drift scenario, birds would be carried eastwards such that they would very likely arrive in the Indian Ocean. None of the tracked birds did this, but instead made directed movements northwards for >2,000 km before, for the most part, following trade winds westwards. While the cues used by juvenile seabirds to navigate are poorly known, we suggest that this initial direction is highly likely to be innate as it was followed by all our tracked juveniles. The same mechanism likely explains the initial bearings of juvenile white-  (Toggweiler, 2009). As for productivity, chlorophyll a concentration has generally increased over the Patagonian Shelf, presumably increasing attraction to this region associated with higher resource availability (Dunstan et al., 2018).

| Consequences of movement patterns for overlap with threats at sea
White-chinned petrels are one of the most common bycaught seabirds in longline fisheries, because they are numerous, compete aggressively for bait, offal and discards, can dive to >10 m, and occur in productive shelf habitats where fisheries are often concentrated (Barnes, Ryan, & Boix-Hinzen, 1997;Cherel, Weimerskirch, & Duhamel, 1996;Weimerskirch, Catard, Prince, Cherel, & Croxall, 1999). Adults from South Georgia winter on the Patagonian Shelf and off southern Chile, both areas of high demersal and pelagic longline fishing effort (Phillips et al., 2006). Overlap of core use areas of non-breeding adults with longline fishing activity (based on satellite AIS data) was therefore predictably high in our study, Watch dataset. Overlap with this fleet was also low when using effort data available from ICCAT, underlining potential gaps in reporting to RFMOs at a regional level. However, we revealed some overlap with longline vessels from Cambodia, China and South Korea, from which there are no published reports of seabird bycatch. Overlap indices are scale-dependent, and by studying overlap at fine spatial and temporal scales, our study highlighted new fleets for which bycatch may be a major concern, emphasizing the pressing need for much more comprehensive monitoring of seabird bycatch rates and uptake of mitigation (Phillips, 2013;Torres, Sagar, Thompson, & Phillips, 2013).
In contrast to adults, juveniles overlapped to a lesser extent with longline vessels fitted with active AIS. A low level of overlap occurred from the first week from fledging; however, it then increased over the following months as juveniles shifted distribution west towards the coast of South America. This has important implications for the dynamics and potential recovery of this threatened population. The naïve behaviour of juvenile seabirds is considered to render them more susceptible to bycatch than more experienced adult life stages (Gianuca, Phillips, Townley, & Votier, 2017). For the first two months, the juvenile white-chinned petrels mostly overlapped with pelagic longline fleets from a variety of flag states operating under the jurisdiction of ICCAT; south of 25°S, these are required to use at least two of three mitigation measures: night setting, bird-scaring (Tori or streamer) lines and line weighting (Gilman, 2011;ICCAT, 2009). However, 95% of these vessels lack independent monitoring, observer coverage is poor, and, as a result, these measures are not implemented consistently (Brothers & Robertson, 2019;Gilman, 2011). It is thus likely that incidental mortality of juveniles occurs, which may be a major contributing factor to the population decline recorded at South Georgia (Berrow, Croxall, & Grant, 2000).

| CON CLUS ION
Here, we demonstrated that a mechanistic movement model can be used to better understand the environmental drivers of divergent movement strategies within seabird populations. Moreover, due to their focus on underlying processes, mechanistic frameworks offer promising applications for predicting how individuals may be exposed to and respond to changes in their environment (Bocedi, Zurell, Reineking, & Travis, 2014;Evans et al., 2019;Leroux et al., 2013). It is also important that scientists continue tracking individuals across life-history stages to understand variation in the drivers of habitat use among and within species, and any consequences for susceptibility of each age class to different threats (Carneiro et al., 2020;Clay et al., 2019;Hazen et al., 2012). In the context of mitigating fisheries bycatch in seabirds, the development of exciting new bio-logging tools (e.g. loggers which detect ship radar; Weimerskirch, Filippi, Collet, Waugh, & Patrick, 2018) are paving the way for an increased understanding of marine predator-fisheries interactions at fine spatial-temporal scales, and will be crucial in setting future management priorities.

ACK N OWLED G EM ENTS
We are grateful to all the fieldworkers involved in the device deployment and retrieval and to Andy Wood for database support. We also thank the referees and the editors for their comments, which helped improve the manuscript. This study represents a contribution to the

PE E R R E V I E W
The peer review history for this article is available at https://publo ns.com/publo n/10.1111/ddi.13130.

DATA AVA I L A B I L I T Y S TAT E M E N T
The datasets supporting the conclusions of this article are available for download from the BirdLife International Seabird Tracking Database (http://seabi rdtra cking.org/mappe r/contr ibutor.php?contr ibutor_id=361); dataset ids: 1386, 1389 and 1500.